Are women in supervisory positions more discriminated against? A - - PowerPoint PPT Presentation

IMAGEN “Macroeconomic consequences of Gender discrimination: state of the art” Universidad de Santiago de Compostela July 18 19th 2013 Universidad de Santiago de Compostela - July 18-19th 2013

Are women in supervisory positions more di i i t d i t? A lti i l discriminated against? A multinomial approach pp

• Marco Biagetti* and Sergio Scicchitano* -

*Ministry for Economic Development, Department for the Development and the Economic Cohesion.

The views expressed in this article are those of the authors and, in particular, do not necessarily reflect those of the Ministry of Economic Development.

Outline Outline

• 1. Introduction: motivation and goals

2 Previous literature

• 2. Previous literature
• 3. Our procedure: 2+1 steps using EU-SILC data

4 h d l

• 4. The model
• 5. Results
• 6. Conclusions: possible explanations, policy

indications and further research indications and further research

Motivation: gender wage gap at a i l l supervisory level

• It is well known that equality between men and women is a

worldwide priority in the current economic policy, p y p y, particularly for the European Commission (EC),

• Nevertheless, it is also still evident that, despite women’s

higher education than men’s and their increasing participation in the labor market, they are still largely under-represented in th hi h hi hi l iti d ff f i ifi t the high hierarchical positions and suffer from a significant pay gap particularly at a supervisory level (EC 2003, 2009).

3

Goals of the paper: 3 questions p p q

1) Are gender wage gap and discrimination against ) g g g p g women higher among supervisory positions than among non supervisory? g p y 2) Is there any selectivity bias for both men and women for participating to the labor market and working as for participating to the labor market and working as supervisors or non supervisors? 3) Therefore is a two stages approach more precise as 3) Therefore, is a two stages approach more precise, as well as more suitable, than a single OLS in order to give us an unbiased answer to the first two questions? give us an unbiased answer to the first two questions?

4

Key-point in the procedure

To control for a selectivity bias affecting the sample selection: two main reasons:

• 1. When general women’s wages are examined, the possibility that

unobservable factors influence selection into the sample usually remains a serious obstacle .

• 2. When the GWG for women in a supervisory position is examined

and compared with that of non supervisor women, the selection term becomes much more relevant in terms of unbiased estimates. This fact occurs for a couple of other reasons This fact occurs for a couple of other reasons.

1. First, if we are interested in examining women’s behavior in a particular working condition x, the most appropriate potential outcome is even threefold - working in x, not working in x, not working at all - so that we have to compute two selection terms (Rodgers 2004). 2 When that particular working condition x is the supervisory position where 2. When that particular working condition x is the supervisory position, where women are traditionally underrepresented, the Inverse of the Mill’s Ratio (IMR) is expected to be particularly significant.

5

Previous empirical literature: the GWG among managers GWG among managers

• Despite the general GWG having been largely investigated, the analysis of a wage

differential among managerial workers has certainly drawn much less attention differential among managerial workers has certainly drawn much less attention.

• Bertrand and Hallock (2001) study the GWG in the top-level management of

U.S. corporations. The GWG was at least 45%.

• Muñoz-Bullón (2010) finds similar results: the GWG was equal to 47%,
• Bertrand et al (2010) study gender differences in the career dynamics of MBAs

who graduated from a top US business school from 1990 to 2006. The GWG is g p equal to almost 60 log points a decade after completing the MBA.

• Baldwin et al (2001), Oaxaca et al. (2013): hierarchical segregation.
• Scicchitano (2012) and Biagetti and Scicchitano (2013 forthcoming) apply a

Scicchitano (2012) and Biagetti and Scicchitano (2013 forthcoming) apply a counterfactual decomposition analysis to investigate the GWG among managers in UK and Spain respectively:

UK: clear a U shaped pattern both sign sticky floor and glass ceiling effects – UK: clear a U-shaped pattern, both sign. sticky floor and glass ceiling effects. – Spain : the glass ceiling constitutes a bigger problem than the sticky floor.

Previous empirical lit: sample selection p p

• A couple of papers examine the GWG for the managerial position

after controlling for sample selection after controlling for sample selection

• Holst and Busch (2009) demonstrate that in Germany, after taking

into account a twofold selection effect for a leadership position, only p p , y

• ne third of the gender wage differential could be explained.
• Watson (2009) studies the GWG among managers in Australia by

g g y also controlling for a threefold selection bias in the labor market participation as a manager or non manager. He finds that female b t 25 t l th th i l t t d managers earn about 25 per cent less than their male counterparts and somewhere between 70 and 90 per cent of this wage gap cannot be explained explained.

• However, there is no study comparing GWG while at the same time

analyzing the discrimination against women, in both leadership and a a y g t e d sc at o aga st wo e , bot eade s p a d non leadership positions.

7

Previous empirical literature in Italy

• Rustichelli (2005) evaluates a GWG between women and men’s daily

wages equal to 39% on average, 26.9% of which is attributed to differences in rewards.

• Picchio and Mussida (2010) find that, when a control for sample

selection is made, the GWG significantly increases at the top of the wage distribution, where it jumps from 17.3% to 24.5%. Bi tti d S i hit (2012) l t f t l

• Biagetti

and Scicchitano (2012) apply a counterfactual decomposition approach using QR to the wage distribution of managerial workforce in Italy Evidence of both significant sticky managerial workforce in Italy. Evidence of both significant sticky floor and glass ceiling effects

• However, although in this country the empirical literature on the

, g y p general GWG is somewhat extensive, the comparison between hierarchical positions after controlling for a possible sample selection bias is almost completely ignored.

8

Procedure

• As we noted above a crucial point is to control for the selectivity bias
• In doing so, we choose to adopt a two ( +1) step procedure. In the

g , p ( ) p p first step, we correct for a threefold selectivity bias through a multinomial logit. This selectivity may affect the participation to the labor market as non supervisory or supervisory for both males and females. I th d t ti t th ti b t ki i t

• In the second step we estimate the wage equation by taking into

account the eventual significant selectivity terms.

• This method makes us able to verify the unadjusted and the
• This method makes us able to verify the unadjusted and the

(multiple-selection) adjusted GWG between supervisory and non supervisory jobs. Furthermore, we can verify whether or not the supervisory jobs. Furthermore, we can verify whether or not the discrimination against women is higher among managerial positions compared to the non managerial through the B-O decomposition.

9

Data Data

• We use last European dataset, the Community

Statistics on Income and Living Conditions g survey (EU-SILC), whose last wave, for which the reference year is 2007 is available which the reference year is 2007, is available since march 2009.

• To the best of our knowledge, this dataset has

never been used yet in studying the analysis y y g y

• f the GWG between supervisory and non

supervisory positions supervisory positions.

10

Definition of manager/supervisor g p

• Following Acemoglu and Newman (2002) and

Beaudry and Francois (2010) we define the manager/supervisor as the employee whose job g p p y j description is associated to the responsibility in the

• rganization

and monitoring

• f
• ther

the

• rganization

and monitoring

• f
• ther

employees.

• EU-SILC identifies such a working condition

with a supervisory role where supervisory p y p y responsibility includes formal responsibility for coordinating a group of other employees coordinating a group of other employees

11

Methodology: first stage gy g

Threefold potential outcome, first stage (selection stage): (NW, W, WM) => that is why we divide our dependent variable into 3 categories (i.e. multinomial logit), to get h f ll i b bili i d the following probabilities and compute IMRs

1

Z is matrix containing personal categorical variables: age level of

( ) ( ) ( )

ij ij ij ij

Z Z Y ε β β + + + = =

2 1

exp exp 1 1 Pr

variables: age, level of education attained, consensual union, health, household type (linked to the number

( ) ( ) ( ) ( )

ij ij k ij

Z k Y ε β + = = exp Pr

(linked to the number

• f persons and

dependent children living in a family) and citizenship i=ith individual k=1,2 i.e. W and WM and j=males, females ε~N(0,1)

( ) ( ) ( )

ij ij ij ij

Z Z k Y ε β β + + +

2 1

exp exp 1 Pr

citizenship.

Methodology 2: second stage

Second stage (wage stage): we bring along something useful (the IMRs) from the first stage in order to get unbiased OLS estimates i k (19 9) d i (200 ) d hi f h as in Heckman (1979) and Bourguignon (2007). We do this for the 2 genders and 2 categories (W & WM).

k added in order to take into account diversity of coefficients by category

• ther than sex

standard normal density

Error term ζ ~N(0 σ )

( ) ( ) [ ] ( ) [ ]

ijk ijk jk ijk jk ijk jk jk ijk jk jk jk jk ijk jk ijk

c X Z H Z H X W ζ λ α β β φ ρ σ α + + = Φ + = ln

ζijk~N(0,σjk),

( )

j j j

Normal cdf IMRs computed before Sd of the error term for wage j

Correlation between error term in the

Matrix of regressors

selection equation and and that in the equation for wage j. If it’s ≠0 then the selection exists! selection exists!

( )

ρ ζ ε =

ijk ijk

corr ,

Explanation of the sign of λ

λ is the selection effect. If >0, more able people are likely to enter the labor market and get higher wages. More precisely, those who select or are selected for the labor market - be they managers or non managers - obtain a larger remuneration than a random drawing from the population of men and women with a comparable set of characteristics would get => LABOR MARKET CAN BE REGARDED AS FAIR If <0, then the less able people are likely to enter the labor market and get higher wages. I.e. those who select or are selected for the labor market get lower wages than the rest of the population of men and women with a comparable set of characteristics drawn randomly. => LABOR MARKET CAN BE REGARDED AS UNFAIR Put it another way, market rules are not the only one prompting the decision to get a job

• r to reach a bargain, while other things matter.

Methodology 3

Last stage: the gender wage gap G with and without significant selection

Methodology 3

Last stage: the gender wage gap G with and without significant selection terms are computed for non managers and managers and split up into endowment, coefficients and interaction effects singled out using the classic threefold B-O decomposition

( ) [ ] ( ) ( ) ( ) ( ) [ ] ( )

F M B M F M F F F M

X E X E X E X X E G α α α α α − − + − + − =

' ' '

Endowments effect: it measures the expected change of women’s mean wage if they had the same predictor levels as men or vv. Coefficients effect: it measures the expected change of women’s mean outcome if they had the same coefficients as men: sort of di i i ti Interaction effect due to differences in endowments and coefficients and existing at the same time for males and females discrimination

Sensitivity analysis

• A Propensity Score Matching (PSM)

method is added to our analysis in order to

– Verify the goodness of choice of the variables Verify the goodness of choice of the variables in the selection equation, by assessing the satisfaction of the so called balancing property; sat s act o o t e so ca ed ba a c g p ope ty; – Evaluate the GWG by means of some PSM techniques techniques.

16

Results

RRR P>|z| RRR P>|z| RRR P>|z| RRR P>|z| Age 36-45 1.728 0.004 2.089 0.000 3.747 0.000 4.463 0.000 Age 46 55 1 393 0 126 1 953 0 003 3 565 0 000 4 586 0 000

• Tab. 3. Multinomial Logit Equations of Employment Choice by Males and Females

Male Female Non supervisory Supervisory Non supervisory Supervisory Age 46-55 1.393 0.126 1.953 0.003 3.565 0.000 4.586 0.000 Age 56-65 1.295 0.392 1.383 0.300 4.433 0.001 7.379 0.000 ISCED 3 & 4 1.277 0.106 3.570 0.000 2.241 0.000 5.051 0.000 ISCED 5 0.715 0.085 4.460 0.000 1.069 0.684 4.189 0.000 Union without legal basis 3.625 0.004 9.153 0.000 2.795 0.002 6.307 0.000 Union with legal basis 3.668 0.000 9.028 0.000 4.689 0.000 6.656 0.000 Good health 1 248 0 210 1 409 0 065 1 148 0 407 1 716 0 005 Good health 1.248 0.210 1.409 0.065 1.148 0.407 1.716 0.005 Fair health 0.692 0.098 0.653 0.071 1.264 0.341 2.086 0.007 Bad health 0.451 0.016 0.511 0.062 0.272 0.000 0.342 0.003 Very bad health 0.420 0.185 0.163 0.036 0.429 0.395 0.842 0.877 2 adults, no dep. child., both adults under 65 years 0.446 0.020 0.275 0.000 0.431 0.018 0.276 0.001 2 ad no dep child at least one 2 ad., no dep. child., at least one adult 65 years or more 0.421 0.030 0.221 0.000 0.376 0.043 0.209 0.003 Other households without dep. child. 0.298 0.000 0.165 0.000 0.331 0.000 0.125 0.000 Single parent hous., one or more

• dep. child.

1.17E+08 0.000 1.19E+08 0.000 0.407 0.023 0.288 0.003 2 adults, one dependent child 0.243 0.000 0.117 0.000 0.175 0.000 0.081 0.000 adu ts, o e depe de t c d 0. 3 0.000 0. 7 0.000

• 0. 75

0.000 0.08 0.000 2 adults, two dependent children 0.675 0.392 0.393 0.047 0.124 0.000 0.053 0.000 2 adults, three or more dep. child. 0.251 0.007 0.108 0.000 0.120 0.000 0.037 0.000 Other households with dep. child. 0.214 0.000 0.089 0.000 0.187 0.000 0.066 0.000 Italian citizenship 2.13E-08 0.000 3.23E-08 0.000 1.655 0.369 6.812 0.011 Other citizenship 5.97E-08 0.000 1.24E-08 0.000 1.512 0.493 1.032 0.969 LR chi2(42) 1310.95 741.79 ( ) Prob > chi2 0.000 0.000 Pseudo R2 0.112 0.090 Number of obs 8,323 6,815

Notes: Odds ratios, also known as relative risk ratios (RRR), are reported.

• Outcome variable: working as a manager; or working otherwise; or not working at all. Base (reference) category: not working at all. P values in italics.
• Observations are weighted by EU-SILC personal cross-sectional weights.
• Omitted categories are: Aged 25 to 35; ISCED 0-2; Single; Very good health; One person household; Any European union country (EU25) except Italian.
• Source: elaboration from EU-SILC 2007, available since March 2009.

WHAT’S ON THE ML TABLE? (Results are those expected )

The relative risk ratio (RRR) of working in non supervisory or supervisory positions with respect supervisory or supervisory positions with respect to not working at all increases with:

• Age (more important for sup although not

Age (more important for sup. although not monotonically);

• Education (more important for sup.);

( p p );

• Unions (be they with or without a legal basis);

RRR decreases with:

• Bad health;
• Numerous family especially for women (see for

y p y ( example what happens in a classical 2+2 family);

• Citizenship other than the Italian.

Results 2

• Tab. 4. OLS results: non supervisory

Unadjusted Adjusted

Results 2

Coef. P>|t| Coef. P>|t| Coef. P>|t| Coef. P>|t| ISCED 3 & 4 0.083 0.000 0.117 0.000 0.058 0.000 0.102 0.000 ISCED 5 0.199 0.000 0.183 0.000 0.149 0.000 0.137 0.000 Exp 0.019 0.000 0.017 0.000 0.019 0.000 0.017 0.000 Exp 2 0 000 0 000 0 000 0 000 0 000 0 000 0 000 0 000 Males Females Males Females Exp_2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Part

• 0.456

0.000

• 0.402

0.000

• 0.457

0.000

• 0.401

0.000 Union without legal basis

• 0.006

0.740 0.027 0.193

• 0.008

0.673 0.000 0.998 Union with legal basis

• 0.067

0.000

• 0.007

0.482

• 0.057

0.000

• 0.014

0.147 Italian citizenship 0.019 0.775

• 0.094

0.039 0.001 0.986

• 0.121

0.008 Other citizenship

• 0.047

0.482

• 0.132

0.006

• 0.033

0.617

• 0.124

0.009 Limited 0.081 0.005 0.060 0.092 0.078 0.007 0.068 0.058 Not limited 0.107 0.000 0.090 0.007 0.103 0.000 0.099 0.003 Size 11-19 0.065 0.000 0.080 0.000 0.065 0.000 0.081 0.000 Size 20-49 0.071 0.000 0.091 0.000 0.072 0.000 0.090 0.000 Size > 50 0.116 0.000 0.156 0.000 0.116 0.000 0.156 0.000 Lambda 0 349 0 002 0 683 0 000 Lambda 0.349 0.002 0.683 0.000 Cons 2.108 0.000 1.587 0.000 2.008 0.000 1.368 0.000

• N. Obs

4914 4626 4914 4626 Prob > F 0.000 0.000 0.000 0.000 R-squared 0.400 0.538 0.401 0.540 Adj R-squared 0.393 0.533 0.395 0.535 j q Root MSE 0.270 0.299 0.270 0.299

• Note. 27 dummies for occupations and 13 dummies for sectors included, but not reported.
• Omitted categories are: ISCED 0-2; full-time; Single; Any European union country (EU25) except Italian; Strongly limited in activities because of health

problems; local unit size 1-10.

• Source: elaboration from EU-SILC 2007, available since March 2009.

Ob ti i ht d b EU SILC l ti l i ht

• Observations are weighted by EU-SILC personal cross-sectional weights.

WHAT’S ON THE NON SUPERVISORY LOG WAGE TABLE WITH AND WITHOUT ADJUSTMENT?

Wage increases with: Wage increases with:

• Education;
• Experience;

p ;

• Being not limited or only partially limited in body ability;
• Size of enterprise (especially for women);
• Lambda is positive and significant for both sexes, even

though its effect is clearly stronger for women than for men. Wage decreases with:

• Part-time work (especially for non supervisory men);
• Union under legal basis (while common-law marriages are

found to be non significant); found to be non significant);

• Being non national (for women).

Results 3

• Tab. 5. OLS results: Supervisory

Unadjusted Adjusted Males Females Males Females Coef. P>|t| Coef. P>|t| Coef. P>|t| Coef. P>|t| ISCED 3 & 4 0.121 0.000 0.151 0.000 0.090 0.002 0.130 0.000 ISCED 5 0.344 0.000 0.222 0.000 0.285 0.000 0.179 0.000 Exp 0.024 0.000 0.025 0.000 0.023 0.000 0.024 0.000 Males Females Males Females Exp_2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 Part

• 0.258

0.000

• 0.462

0.000

• 0.257

0.000

• 0.459

0.000 Union without legal basis

• 0.055

0.094 0.018 0.639

• 0.060

0.071

• 0.003

0.947 Union with legal basis

• 0.100

0.000 0.018 0.392

• 0.082

0.000 0.017 0.439 Italian citizenship

• 0 252

0 020

• 0 130

0 415

• 0 254

0 019

• 0 163

0 311 Italian citizenship

• 0.252

0.020

• 0.130

0.415

• 0.254

0.019

• 0.163

0.311 Other citizenship

• 0.118

0.378

• 0.003

0.989

• 0.075

0.583 0.007 0.968 Limited 0.091 0.119 0.181 0.038 0.096 0.103 0.178 0.041 Not limited 0.104 0.050 0.176 0.037 0.106 0.046 0.178 0.035 Size 11-19 0.032 0.205 0.087 0.010 0.031 0.219 0.088 0.009 Size 20-49 0.074 0.003 0.109 0.001 0.074 0.003 0.109 0.001 Size > 50 0.126 0.000 0.185 0.000 0.126 0.000 0.187 0.000 Lambda

• 0.269

0.122

• 0.384

0.060 Cons 2.108 0.000 2.002 0.000 2.306 0.000 2.318 0.000

• N. Obs

2125 1027 2125 1027

• N. Obs

2125 1027 2125 1027 Prob > F 0.000 0.000 0.000 0.000 R-squared 0.445 0.582 0.445 0.584 Adj R-squared 0.431 0.560 0.431 0.561 Root MSE 0.331 0.305 0.331 0.305

WHAT’S ON THE SUPERVISORY LOG WAGE TABLE WITH AND WITHOUT ADJUSTMENT? (MORE OR LESS JUST LIKE BEFORE)

• Wage increases with:

g

– Education (adjustment decreases the effect as expected); – Experience; – Union size – Union size

• Wage decreases with:

– Part-time work; – Legal union;

• Enterprise size not always significant
• Lambda is negative and significant only for women

Table 5 Blinder-Oaxaca decomposition. Raw, unadjusted , adjusted by IMRs wage gap and PSM. Supervisory and not supervisory positions Not Supervisory Supervisory exp(b) % exp(b) % Raw Males 10.767 15.918 Females 8.858 12.176 Difference 1.909 *** 17.7% 3.742 *** 23.5%

Results

Difference 1.909 17.7% 3.742 23.5% (0.074) (0.289) Unadjusted Males 10.097 *** 14.652 *** (0.050) (0.141) Females 8.093 *** 11.205 *** (0.052) (0.163) Difference: 2.004 *** 19.8% 3.446 *** 23.5% (0.010) (0.023)

4

( ) ( ) Endowments 1.047 *** 20.9% 1.062 22.5% (0.013) (0.044) Coefficients 1.135 *** 57.2% 1.223 *** 75.2% (0.092) (0.022) Interaction 1.050 *** 21.9% 1.006 2.3% (0.013) (0.042) Endowments of which ISCED 3 & 4 0.989 ***

• 24.3%

0.998

• 2.6%

(0.002) (0.003) ISCED 5 0.983 ***

• 36.8%

0.981 ***

• 31.9%

(0.002) (0.005) Exp 1.041 *** 87.1% 1.053 *** 84.9% (0.006) (0.013) Adjusted Males 8.783 *** 17.128 *** (0.392) (1.738) Females 6.304 *** 14.447 *** (0.353) (1.962) Difference: 2.479 *** 28.2% 2.681 15.7% (0.010) (0.201) Endowments 1.054 *** 15.9% 1.066 37.7% (0.013) (0.044) Coefficients 1.257 *** 69.0% 1.106 59.0% (0.092) (0.186) I i 1 051 *** 15 1% 1 006 3 3% Interaction 1.051 *** 15.1% 1.006 3.3% (0.013) (0.042) Endowments: of which ISCED 3 & 4 0.990 ***

• 18.5%

0.999

• 2.1%

(0.002) (0.002) ISCED 5 0.987 ***

• 24.1%

0.985 ***

• 24.2%

(0.002) (0.005) Exp 1.042 *** 77.8% 1.051 *** 76.8% (0 006) (0 013) (0.006) (0.013) Nearest Neighbor Propensity Score Matching without replacement* Males 10.176 14.634 Females 8.119 10.979 Difference: 2.057 *** 20.2% 3.655 *** 25.0% (0.009) (0.004)

• N. Obs.

9540 3152 Males 4914 2125 Females 4626 1027 Females 4626 1027

• Notes. Coefficients are obtained throught "Oaxaca eform": the "Oaxaca" coefficients can be obtained

by taking the ln. Standard errors in brackets. Significance: * p<0.10. ** p<0.05. *** p<0.01. Relative percentages for endowments, coefficients, interaction, ISCED 3 & 4, ISCED 5 and exp refer to coefficients in "Oaxaca".Gender wage gap is calcultated as (hourly male wage - hourly female wage)/ hourly male wage *As for PSM, Kernel-Epanechnikov and caliper techniques have also been used: their results are not different from those achieved through the NN technique

Results 5

• Fig. 2a. Gender wage gap for not supervisory positions: unadjusted and adjusted

57 2% 69 0% 21.9% 15.1% 19.8% 28.2% 20 25 30 35 40 60% 80% 100%

Interaction Coefficients

20.9% 15.9% 57.2% 69.0% 5 10 15 20 0% 20% 40%

Coefficients Endowments Wage gap

2 3% 3 3% 25 100%

• Fig. 2b. Gender wage gap for supervisory positions: unadjusted and adjusted

Unadjusted Adjusted

75.2% 59.0% 2.3% 3.3% 23.5% 15.7% 15 20 25 60% 80% 100%

Interaction Coefficients

22.5% 37.7% 5 10 0% 20% 40%

Endowments Wage gap

24

0% Unadjusted Adjusted

R lt 6 iti it l i Results 6: sensitivity analysis

• The PSM exercise has been corroborated by

the general respect of the balancing property which suggests us that no less p p y gg parsimonious specification of the selection equation is needed equation is needed.

• It shows a difference of 20.2% among non

supervisors and 25% among supervisors.

25

Conclusions:

• In this paper we have demonstrated that in Italy at a

In this paper, we have demonstrated that, in Italy, at a supervisory level, women suffer from a higher wage gap with respect to those in a non supervisory position. g p p p y p

• This evidence can be explained at least partly by

personal endowments, even after considering many p , g y control variables.

• In particular for women with supervisory tasks…

p p y

The “unfairness” for female managers g

Th l ti it bi i ti d i ifi t l f h i

• The selectivity bias is negative and significant only for women having

supervisory tasks.

• This result means that, at the upper level of jobs, the less able women are likely

pp j y to enter the labor market and get higher wages. Putting it in another way, those who are selected for the labor market get lower wages than a random drawing from the same population of women with a comparable set of characteristics from the same population of women with a comparable set of characteristics would get.

• That result also allows us to regard the supervisory labor market of women as

“ f i ” i h k t l t th l i th d i i t “unfair”, i.e. where market rules are not the only one governing the decision to get a career advancement or to reach a bargain, while other things matter. As a confirm, when accounting for the selectivity bias at the supervisory level for males and females, the B-O decomposition shows that the gender wage gap and the discrimination could be clearly reduced and made not significant.

27

2 possible explantions: childcare p p

1 I i ll k h i I l h f hild d 3 1 It is well known that in Italy the percentage of children under 3 years of age who are in childcare is relatively quite low (Del Boca and Locatelli 2008 Nicodemo 2009) and Locatelli 2008, Nicodemo 2009). Therefore, as also partially argued by Picchio and Mussida (2010), the lack of a structured framework of childcare may (2010), the lack of a structured framework of childcare may have relevant implication for the women’s participation to the labor market even at a supervisory level, as it may induce women to prefer household management rather than job tasks. Anyway, it is reasonable to argue that the absence of childcare l di l i ff l hi h hi hi l l l b may also display its effect not only at high hierarchical levels but even at the lower, hence other forces should be called to explain such evidence such evidence.

28

Gender stereotypes

2 In particular, idiosyncratic, sociological and cultural reasons may have a major role in leading to such results for the high hierarchical levels, because the supervisory positions are traditionally considered as a men’s prerogative (OECD 2002) traditionally considered as a men s prerogative (OECD 2002). In this framework, stereotypes may be much likely to constitute the most significant barriers to women’s career constitute the most significant barriers to women s career advancement and appointment to managerial positions, thus generating a gender-segregated labor market.

29

Policy indications y

• Persisting higher gender gaps at the supervisory level,

Persisting higher gender gaps at the supervisory level, as they are pointed out in this paper, confirm the importance of eliminating economic and cultural importance of eliminating economic and cultural barriers to women’s full participation and carriers in the labor market. the labor market.

• In this context, both policies aimed at reconciling

work and family and at fighting gender stereotypes work and family and at fighting gender stereotypes may have a direct impact on employment, earnings, and positions of women in the labor market and may be positions of women in the labor market, and may be able to reduce discriminations against them, particularly when employed with higher responsibility particularly when employed with higher responsibility tasks.

Further research

• Extend

the analysis to the all wage distribution (the current analysis is based

• n the conditional mean):

)

– Counterfactual decomposition analysis

E i th E t i

• Examine other European countries.

– EU-SILC is quite useful for comparisons across a number of countries.

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Th k h Thank you very much.. sergio.scicchitano@tesoro.it g @